Mind The Gap! - Expectation Vs Reality In Workforce Analytics

With due apology for the misappropriation of a famous Jane Austen literary reference, ‘it is a truth universally acknowledged’ that a person in possession of good data has a significant advantage in their ability to influence their environment. Whether it’s selecting baseball players to win matches or deciding which product to buy, there are few who would argue that they are impervious to the influence of data-driven insight.

Ubiquity Of Data

Data is all around us, in our private lives and at work. We create data for others to use, we consume data to help make decisions and we often rely on complex systems to predict and project an appropriate outcome. If you need convincing of this, take a typical day and count the number of times some form of data analysis has influenced what you do. As I write this article for example, analytics in my word processing software is full of suggestions on ways in which I can improve my grammar. This type of interaction is becoming more prevalent as technology advances and generative AI becomes de rigueur. In turn, this has given us a heightened expectation about the direction of travel, particularly in the area of work. The future of work is data-driven, but we can sometimes feel unsure of who is in ultimate control. Some people are excited about such advances, whereas others are more cautious.

Utility Of Data

The objective of this article is to illustrate that data is not only one of the most influential resources at our disposal, but also one of the most accessible and, therefore, should not be ignored.

For those not convinced that data analytics warrants attention in your area of work, let’s first look at the ‘why’ before moving on to suggestions on how to make tangible progress.

At a fundamental level, the benefits of utilising data fall into two broad categories:

1. It helps make better informed decisions. Instead of acting purely on intuition, when we have data available, we are better able to make an informed call. It is worth considering the different forms this can take:

• Delivering the relevant timely data and information, potentially leading to reduced risk of error or decision bias, e.g., live and interactive dashboards

• Delivering likely outcomes from different actions, leading to an increased understanding of possible consequences, e.g., predictive analytics

• Identifying blockers or enablers with causal links to intended objectives, leading to a better understanding of the ‘levers to pull’ for targeted results, e.g., determining the benefits that have a causal effect on wellness.

2. It influences behaviour by encouraging action. The other less considered benefit is that delivering the right data doesn’t just increase our knowledge, it causes or motivates people to behave in a certain way.

A good example of this is seen when considering speed cameras. Not the kind that take a picture and trigger a fine, rather, the sort that displays a smiley or sad face depending on speed. Despite the fact there is no fine or any other consequence, studies have shown (1) that simply displaying a happy face when travelling within the speed limit works in curtailing the speed of drivers. I believe the reason is because people are receiving immediate feedback data on something they can influence. When the outcome is something the individual wants, this often encourages people to act.

Relevant data displayed in a timely way that resonates with the recipient, can lead to changes in behaviour. In the work context this can look like:

• Sending regular updates on progress against a specific target, or KPIs encouraging action because ‘what is measured gets done’

• Creating rules to automate or nudge particular behaviours is a reminder that encourages action from people who are bought into the bigger picture

• Showing predictions of how actions influence outcomes helps users see the value in what they are about to do.

The use of AI, particularly Machine Learning, has helped improve outputs in both categories and to deliver otherwise unobtainable insights. However, there is much that can be achieved without AI. It is frustrating to consider how many strategic initiatives may have failed because people are too busy to track the outcome required, or how much time may have been wasted when we do what ‘felt right’ in the moment to impact vague targets without the necessary data-driven validation. These instances can be reduced by turning to data. However, all too often many fail to reach for this readily available and effective resource.

Life In ‘The Gap’

Why is the effective use of data not a priority for many organisations?

One of the reasons for this stalemate is that there is a dichotomy at play. Heightened expectation as to the possibilities of utilising data analytics almost always co-exists with a perceived ‘reality check’ about data analytic capabilities. Put another way, there is almost no doubt that there is a data-driven solution, but there is pessimism for many about the reality of what can be done now.

This gap has led to unhelpful pressure from stakeholders due to two competing perceptions:

1. We should be able to achieve ‘mindblowing’ things, and

2. Data analytics is too difficult for my team to competently deliver. These perceptions often lead to inactivity, whilst also leaving a sense of resentment. The key first step for progress is to close the gap between expectation and reality.

We need to shift our thinking, so that our perceptions are more realistic. We should be able to say:

1. There is much value to be gained by focusing on how available data influences the things that matter to us, and

2. With a little effort, not only can our team competently deliver data analytics that enhances our services today, but our people are training to meet the needs of tomorrow.

By shifting our thinking in this way, we will identify opportunities to deliver value and reduce the resentment created by the perception of being ‘left behind’.

Minding The Gap - Step 1: Shift Your Expectations

One reason this is an issue is that data is often only considered in limited and obvious cases. A key first step is therefore to shift expectations to better understand where data can help.

Is It Something Data Can Help With?

Last year, at the Deloitte GES Client Summit, we held a workshop entitled ‘Adopting a data-driven approach in support of your purpose agenda’. We chose this topic because, despite its importance, it is often ignored in the bustle of the day-to-day, often being perceived as too nebulous a concept to get to grips with in a practical way. A recent

Deloitte study found that ‘while roughly two-thirds of employees [in our survey] believe that their organisation has a clearly defined purpose and that it is … true to its purpose, only half (52%) agreed that external purpose communications are consistent with internal practices’. (2) Judging by the workshop feedback we received however, people left the room with increased confidence that data is one of the key resources to use to enhance their purpose agendas.

The reason for this confidence is that the conversation we had shifted the expectation of what data can be used for to include tailored components of importance to each attendee. We thus moved from an inaccessible view of what we should do based on complex techniques, to a more realistic and practical aim of what we can do with available data.

The way that we did this was first to identify the strategic priority befitting the function, then to determine the relevant available data which realises the practical and strategic aim.

The Practical And Strategic Aim

There is little point in measuring data that is not connected to strategic objectives. However, not all strategic priorities have obvious metrics. The first step is therefore to identify components of the strategic objectives that can be measured. An obvious example is wellness, which is notoriously difficult to measure reliably. However, there are peripheral objectives that could be used as a proxy for wellness, such as sickness or retention statistics or uptake of wellness initiatives. Focusing on these proxies enables you to identify metrics that are both practical and strategic that stakeholders can get behind. The next step is to find data that could provide information on the aims in question. It is essential to look at the type of data as well as its accuracy and completeness, together with what would be needed to keep the data in a useable state. Where no data is available, look at what additional data can be sourced. All of this plays an important part in understanding the limitation of what can be delivered, and therefore ensures we are left with a realistic practical aim arising from the data analysis.

By identifying the priority and focusing on the available data, we were able to determine concrete ways in which our functions can use data to help in achieving the strategic priorities.

Do you know if you have data that can help with the realistic practical aims of your team? The impact of shifting your expectation of data analytics to something practical could be significant.

Minding The Gap - Step 2: Shift Your Understanding Of Reality

What about our capabilities, how do we shift our expectations there?

A couple of years ago, I wrote an article for this magazine (3) covering some fundamentals to get the business function ready to enable a strategic impact from people data. The aim of the article was to encourage people to focus on the right foundations.

This time, we will examine some of the most common excuses I hear for why people-functions often feel unready to use data and look at why these perceived barriers should not stand in your way.

Most commonly heard excuses:

1. “Our data is rubbish and of course ‘rubbish in’ leads to ‘rubbish out’” – This makes it sound like bad data is something that cannot be fixed. However, with new data wrangling products, it is becoming easier to address this challenge without big investment. When it comes to imperfect data, a better phrase perhaps is “do not let great be the enemy of good” and look for the value to be drawn from imperfect data.

2. “GDPR rules would mean we can never get access to that data” – When it comes to dealing with the people data of your workforce, data privacy concerns are of course very important. However, often this excuse is used by people who have not thought through the detail needed. It is recommended therefore to ask, ‘Why not?’. All too often the rules are used as an excuse because this is a relatively new frontier. Even where there are legitimate concerns about elements of the data required, you may get access to a portion of it, which could be enough. On a practical level, it is useful to get the data protection officer involved early and often in these matters. If you don’t know who that is, a good start is to find out.

3. “We don’t have the people to deliver this type of project” – No longer is data analytics the sole realm of the specialist. With a bit of aptitude, it is getting increasingly easy to undertake data work with codeless products on the market to cleanse, wrangle, analyse and visualise the data. Do you have a strategy for figuring out what are the required skills and how you can build them?

4. “Is there sufficient Return on Investment (ROI)?” – We do not focus on insight for insight’s sake, so ROI is key to any project. To best maximise ROI, it must be connected to business needs, integrated into ‘business as usual’ processes and be focused on realistic outcomes. Also, it must be agile and change if business needs change.

5. “I’m not convinced that a data-based approach will have an impact” – As previously stated, data is a key lever to pull to achieve your strategic priorities. If you are not convinced, start small and get something done to demonstrate value. If you doubt the value in shifting your reality (and increasing your capability) today, think about where you will be as a function in three years’ time. Do you think it is likely you will be more data-driven? The effort made now will make you and your team more future-ready. With the fast advances in AI, one would argue this is something that cannot be left any longer.

Life In ‘The Overlap’

Once you close the gap between expectation and reality, there is an overlapping sweet spot where analytics is truly delivering tangible value in progressing your strategic priorities.

I hope you can see, not only that data is one of the most influential tools at our disposal, but it can also be one of the simplest and most accessible tools available. Focusing on the right priorities without being distracted by the heightened expectation will mean there is no longer the frustration of being left behind.

Of course, we shouldn’t stop there. As value is clarified and ROI on data analytics is realised, there will come a time for further opportunity to invest in capability. Taking these first steps will help you become ready and in control of that data-driven future, where current data science ‘fiction’ will be as common as spreadsheet files are now in supporting the management of your people. Closing the ‘gap’ should mean we can all be excited about where we are headed.

References:

(1) For example, see - www.wired.com/2011/06/ ff-feedbackloop/

(2) www.deloitte.com/uk/en/insights/topics/ strategy/mind-the-purpose-gap.html

(3)Winter 2020 03 International HR Strategy.pdf (internationalhradviser.co.uk)

ALISTER TAYLOR

Director, Global Workforce Analytics

D: +44 20 7303 0403 alistertaylor@deloitte.co.uk www.deloitte.co.uk/globalworkforce

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